Overview
The Reliability area of the second Strategic Highway Research Program (SHRP 2) has focused on the need to improve travel time reliability on freeways and major arterials. SHRP 2 Project L07, Identification and Evaluation of the Cost-Effectiveness of Highway Design Features to Reduce Nonrecurrent Congestion, focused specifically on design treatments that can be used to improve travel time reliability. The objectives of this research were to (1) identify the full range of possible roadway design features used by transportation agencies to improve travel time reliability and reduce delays from key causes of nonrecurrent congestion, (2) assess their costs and operational and safety effectiveness, and (3) provide recommendations for their use and eventual incorporation into appropriate design guides. The research focused on geometric design treatments that can be used to reduce delays due to nonrecurrent congestion.
Highway agencies tend to address recurrent congestion issues with infrastructure treatments and nonrecurrent congestion with intelligent transportation system treatments. That is, daily demand peaks that cause peak hour congestion are often treated by adding base capacity. Congestion caused by incidents, special events, work zones, weather, demand surges, and other infrequent and unpredictable events are typically addressed by providing travelers with real-time information from traffic management centers. These centers monitor freeways and post information about travel time, lane blockages, and alternate routes to drivers in real time via radio, websites, and message boards. Geometric design treatments that address base capacity issues have been investigated and evaluated thoroughly in the literature. More recently, operationsbased treatments such as real-time traveler information and motorist-assist patrols have been evaluated for their effectiveness at alleviating nonrecurrent congestion. However, there is a gap in the literature regarding the use of geometric design treatments to help reduce nonrecurrent congestion: Project L07 research helps to fill that gap.
Through interviews with highway agencies, the research team identified instances of agencies using design elements to help manage nonrecurrent congestion; however, in most cases these design treatments had not been designed for this purpose. Instead, treatments designed to manage recurrent congestion were applied to nonrecurrent congestion events, frequently in an ad hoc fashion. When major incidents occurred, agencies used whatever tools were at their disposal to minimize the disruption to traffic. Although these tools were often not design elements put in place specifically to address nonrecurrent congestion, the operational concepts behind them helped the research team develop a list of design treatments that could be implemented to help achieve the same goals more effectively. These goals involved minimizing the time that stalled or crash-involved vehicles blocked lanes, adding temporary capacity to alleviate congestion (e.g., by allowing shoulder driving), providing opportunities for vehicles to escape a queue and find a new route (e.g., by using median gates), reducing both primary incidents (such as truck ramps) and secondary incidents (such as extra-height median walls that prevent rubbernecking behavior), and minimizing the negative impact of weather on the road surface (e.g., by using anti-icing systems).
Operational, safety, and benefit–cost analyses of the design treatments were conducted to achieve the research objectives. The traffic operational analysis methodology developed in this research built on work completed in SHRP 2 Project L03, Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies, which preceded this research effort. Project L03 developed models for predicting a travel time index (TTI) at five percentiles (10th, 50th, 80th, 95th, and 99th) along the TTI distribution. The TTI distribution represents the travel time of each trip made across a freeway segment during a long time period (for the present purposes, 1 year) relative to the travel time at free-flow speed. That is, vehicles traveling at free-flow speed have a TTI of 1.0, and vehicles traveling at half the free-flow speed have a TTI of 2.0. A full distribution of TTIs for a segment over the course of 1 year captures the travel time of all the trips made, ranging from trips made under free-flow conditions to those made during extreme congestion. Several measurements of delay and reliability can be made from the TTI distribution. The input variables to the Project L03 TTI models were LHL, a measure of lane hours lost due to incidents and work zones; R0.05″, the number of hours during the year that rainfall is greater than or equal to 0.05 in.; and d/c, the demand-to-capacity ratio for the roadway segment.
The Project L03 models focused primarily on estimating the TTI distributions during peak periods; however, to evaluate the impact of nonrecurrent congestion design treatments on delay and reliability, the analysis needed to include all 24 h of the day. The L07 research team adapted the Project L03 models for use during 1-h time-slices, so that the TTI distribution could be predicted for each hour of the day. In addition, the research team improved on the models in two important ways. First, the Project L03 models were based on data from cities that did not experience significant snowfall, so the present research incorporated a snowfall variable (S0.01″) in addition to the rainfall variable in the models. Second, the Project L03 models were developed for peak hours in large metropolitan areas. This research developed additional models to be used for facilities and hours of the day with lower d/c ratios (i.e., less than 0.8).
The TTI models as modified for the L07 research were used to estimate and plot the cumulative TTI distributions for each hour of the day. The shape of the cumulative TTI curve provides a great deal of information about delay and reliability. To measure the impact that a specific design treatment has on reliability, the research team developed a method of measuring the difference between TTI curves for “untreated” and “treated” conditions. To develop the curve for the treated condition, the impact of the design treatment must be described in terms of the four model input variables. In general, most design treatments affect the LHL variable by minimizing the number of incidents that occur, reducing the time that lanes are closed or blocked by traffic incidents or work zones, or providing extra capacity during events that close lanes. Hours of rain or snowfall cannot be affected by design treatments, but their impacts on lane capacity can be affected by design treatments such as snow fences and anti-icing treatments. Some design treatments also affect the d/c ratio. Once the impacts on these variables are determined for a given design treatment, the delay reduction and improvement in reliability can be measured by analyzing the difference between the two TTI curves.
For the safety analysis of nonrecurrent congestion treatments, this research explored the relationship between congestion and safety—specifically the relationship between level of service (LOS) and crash frequency—and developed a mathematical model to quantify the increase in crash frequency at all severity levels as LOS worsens. Crash frequency is lowest around LOS B and C, but begins increasing through LOS D, E, and F. This relationship indicates that if improvements can be made to LOS (by decreasing congestion), crash frequency will decrease. Therefore, design treatments that reduce congestion also improve safety.
Many design treatments have direct safety benefits in that they reduce the frequency of primary or secondary incidents on the road, but design treatments that also reduce congestion have an indirect safety benefit that can be estimated by using the safety–congestion relationship.
The third treatment analysis was a benefit–cost evaluation for the various design treatments. To calculate treatment benefits, three main components are considered: delay savings, reliability improvement, and safety improvement. By using the untreated (base condition) TTI curve and the treated (after treatment implementation) TTI curve, a reduction in delay due to treatment implementation can be calculated. This measurement, which is expressed in vehicle hours, can be converted to dollars by assigning a monetary value to travel time. Many agencies have a default value that is typically used to convert delay hours to economic cost in dollars. A change in reliability can also be determined on the basis of the shift in TTI cumulative curves from untreated to treated conditions. In this project, reliability was quantified as the standard deviation of the travel time distribution, converted into units of hours. There is no consensus in the literature on how this measure should be valued in economic terms, but one common method is to use a reliability ratio. A reliability ratio is the ratio of the value of reliability to the value of time. By defining this ratio as a fixed number, the value assigned to reliability is always a multiple of the value of time. Just as the value of time may vary from one user group to the next (such as freight or peak hour commuters), so too can the reliability ratio vary from one group to the next. The research team defined the reliability ratio to be 0.8 for all travelers at all times of day in this research; this value fell within the range of most values presented in the literature.
The results of this research provide a method for incorporating both the economic savings due to delay reduction and the economic savings due to reliability improvement for a design treatment over its life cycle. Design treatments that are commonly used to address recurrent congestion can also be analyzed by using the approach developed in this research, which takes into account not only the delay improvements associated with the treatment, but also the potential improvements to reliability. Taking these benefits into account results in a more accurate valuation of a design treatment’s net present benefit and benefit–cost ratio. In addition, agencies considering removing roadway features beneficial to nonrecurrent congestion in order to alleviate recurrent congestion (such as by converting a shoulder to a driving lane) can use the methods presented in this report and the Analysis Tool to calculate the expected increase in nonrecurrent congestion and decrease in reliability that might be expected due to the change and compare this cost to the benefits achieved for recurrent congestion by adding additional capacity.
In addition to the documentation of the research in this final report, the research plan included the development of two key products: a design guide for nonrecurrent congestion treatments and an information dissemination plan. The Design Guide catalogs the design treatments considered in this research, providing planners, designers, operations engineers, and decision makers with a toolbox of possible options for addressing nonrecurrent congestion through design treatments. The Dissemination Plan provides a strategic approach to disseminating the results of the research to practitioners to increase awareness of the benefits of designing for reliable roadways.
Through the course of conducting the traffic operational analysis and applying reliability models to assess the traffic operational effectiveness of design treatments, the research team also developed a spreadsheet-based analysis tool that uses the procedures described in this report to provide users with a benefit–cost ratio for various nonrecurrent congestion design treatments on the basis of information input by the user about the specific freeway segment on which it will be implemented, as well as about how the treatment is expected to be implemented. This analysis tool, which is accompanied by a user guide, represents a third key product in the research.